Homologous recombination is a fundamental process crucial for proper alignment and segregation of chromosomes during meiosis and the efficacy of natural selection. Across many species, recombination events tend to cluster into narrow 'hotspots', short regions (< 2 Kb) where the crossover rate is much higher than in the surrounding sequence. The reliable identification of these recombination hotspots is a crucial step in understanding the biological basis of recombination rate variation, and in interpreting patterns of genetic variation. Current computational methods for identifying hotspots make biologically unrealistic assumptions and have extremely low power. This proposal focuses on developing improved methods for estimating fine-scale recombination rates and identifying hotspots, and these will be more accurate, more powerful and more general than previous approaches. The new methods will then be applied to whole genome sequence data sets from a wide range of mammals to address several open questions regarding the biological mechanisms governing recombination rate variation, hotspot formation and loss.